Compressed channel estimation for massive MIMO-OFDM systems over doubly selective channels

Doubly selective (DS) channel estimation for the downlink massive multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems is a challenging problem, due to the requirements on high pilots overhead and prohibitive complexity. In this paper, by exploiting the highl...

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Main Authors: Uwaechia, Anthony Ngozichukwuka, Mahyuddin, Nor Muzlifah, Ain, Mohd Fadzil, Abdul Latiff, Nurul Muazzah, Za'bah, Nor Farahidah
Format: Article
Language:English
English
English
Published: Elsevier B.V. 2019
Subjects:
Online Access:http://irep.iium.edu.my/79233/
http://irep.iium.edu.my/79233/19/79233_Compressed%20channel%20estimation%20for%20massive_article_new.pdf
http://irep.iium.edu.my/79233/2/79233_Compressed%20channel%20estimation%20_scopus.pdf
http://irep.iium.edu.my/79233/3/79233_Compressed%20channel%20estimation%20_wos.pdf
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author Uwaechia, Anthony Ngozichukwuka
Mahyuddin, Nor Muzlifah
Ain, Mohd Fadzil
Abdul Latiff, Nurul Muazzah
Za'bah, Nor Farahidah
author_facet Uwaechia, Anthony Ngozichukwuka
Mahyuddin, Nor Muzlifah
Ain, Mohd Fadzil
Abdul Latiff, Nurul Muazzah
Za'bah, Nor Farahidah
author_sort Uwaechia, Anthony Ngozichukwuka
building IIUM Repository
collection Online Access
description Doubly selective (DS) channel estimation for the downlink massive multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems is a challenging problem, due to the requirements on high pilots overhead and prohibitive complexity. In this paper, by exploiting the highly correlated spatial structure of the obtained array response vectors and sparsity of the multipath signal components of the massive MIMO-OFDM channels, a modified spatial basis expansion model (modified-SBEM) is introduced. Thus, using complex exponential (CE-) modified-SBEM (i.e., modified CE-SBEM) can improve the resolution of the angles of departures (AoDs) information to represent the downlink with far fewer parameter dimensions, since the AoDs are much slower than path gains. Subsequently, we jointly design the effective pilot power and pilot placement for sparse channel estimation by means of an extended model. Our design is based on the block-coherence and sub-coherence simultaneous minimization of the measurement matrix associated with the massive MIMO-OFDM system pilot subcarriers. Furthermore, we leverage the sparse nature of the massive MIMO-OFDM system to formulate the quantized AoDs estimation into a block-sparse signal recovery problem, where the measurement matrix is designed based on the estimated virtual AoD. Thus, a new algorithm namely, generalized quasi-block simultaneous orthogonal matching pursuit (gQBSO), is introduced to solve the problem by providing sparse signal reconstruction solution. Simulation results demonstrate that the proposed scheme can effectively estimate the DS channel for massive MIMO-OFDM systems compared with other existing algorithms. For example, at SNR=20 dB for K=4 users, Doppler shift=0.093 with NT=32 antenna size, the adaptive-QBSO algorithm with G-SBEM and the proposed gQBSO with modified-SBEM can realize approximately 75.44%and 85.14% of the NMSE achieved by the oracle estimator with modified-SBEM.
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institution International Islamic University Malaysia
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publishDate 2019
publisher Elsevier B.V.
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spelling iium-792332020-04-15T07:44:49Z http://irep.iium.edu.my/79233/ Compressed channel estimation for massive MIMO-OFDM systems over doubly selective channels Uwaechia, Anthony Ngozichukwuka Mahyuddin, Nor Muzlifah Ain, Mohd Fadzil Abdul Latiff, Nurul Muazzah Za'bah, Nor Farahidah T Technology (General) TK5101 Telecommunication. Including telegraphy, radio, radar, television TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices Doubly selective (DS) channel estimation for the downlink massive multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems is a challenging problem, due to the requirements on high pilots overhead and prohibitive complexity. In this paper, by exploiting the highly correlated spatial structure of the obtained array response vectors and sparsity of the multipath signal components of the massive MIMO-OFDM channels, a modified spatial basis expansion model (modified-SBEM) is introduced. Thus, using complex exponential (CE-) modified-SBEM (i.e., modified CE-SBEM) can improve the resolution of the angles of departures (AoDs) information to represent the downlink with far fewer parameter dimensions, since the AoDs are much slower than path gains. Subsequently, we jointly design the effective pilot power and pilot placement for sparse channel estimation by means of an extended model. Our design is based on the block-coherence and sub-coherence simultaneous minimization of the measurement matrix associated with the massive MIMO-OFDM system pilot subcarriers. Furthermore, we leverage the sparse nature of the massive MIMO-OFDM system to formulate the quantized AoDs estimation into a block-sparse signal recovery problem, where the measurement matrix is designed based on the estimated virtual AoD. Thus, a new algorithm namely, generalized quasi-block simultaneous orthogonal matching pursuit (gQBSO), is introduced to solve the problem by providing sparse signal reconstruction solution. Simulation results demonstrate that the proposed scheme can effectively estimate the DS channel for massive MIMO-OFDM systems compared with other existing algorithms. For example, at SNR=20 dB for K=4 users, Doppler shift=0.093 with NT=32 antenna size, the adaptive-QBSO algorithm with G-SBEM and the proposed gQBSO with modified-SBEM can realize approximately 75.44%and 85.14% of the NMSE achieved by the oracle estimator with modified-SBEM. Elsevier B.V. 2019-10 Article NonPeerReviewed application/pdf en http://irep.iium.edu.my/79233/19/79233_Compressed%20channel%20estimation%20for%20massive_article_new.pdf application/pdf en http://irep.iium.edu.my/79233/2/79233_Compressed%20channel%20estimation%20_scopus.pdf application/pdf en http://irep.iium.edu.my/79233/3/79233_Compressed%20channel%20estimation%20_wos.pdf Uwaechia, Anthony Ngozichukwuka and Mahyuddin, Nor Muzlifah and Ain, Mohd Fadzil and Abdul Latiff, Nurul Muazzah and Za'bah, Nor Farahidah (2019) Compressed channel estimation for massive MIMO-OFDM systems over doubly selective channels. Physical Communication, 36. ISSN 1874-4907 https://www-sciencedirect-com.ezproxy.um.edu.my/science/article/pii/S1874490719302216?via%3Dihub#! 10.1016/j.phycom.2019.100771
spellingShingle T Technology (General)
TK5101 Telecommunication. Including telegraphy, radio, radar, television
TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices
Uwaechia, Anthony Ngozichukwuka
Mahyuddin, Nor Muzlifah
Ain, Mohd Fadzil
Abdul Latiff, Nurul Muazzah
Za'bah, Nor Farahidah
Compressed channel estimation for massive MIMO-OFDM systems over doubly selective channels
title Compressed channel estimation for massive MIMO-OFDM systems over doubly selective channels
title_full Compressed channel estimation for massive MIMO-OFDM systems over doubly selective channels
title_fullStr Compressed channel estimation for massive MIMO-OFDM systems over doubly selective channels
title_full_unstemmed Compressed channel estimation for massive MIMO-OFDM systems over doubly selective channels
title_short Compressed channel estimation for massive MIMO-OFDM systems over doubly selective channels
title_sort compressed channel estimation for massive mimo-ofdm systems over doubly selective channels
topic T Technology (General)
TK5101 Telecommunication. Including telegraphy, radio, radar, television
TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices
url http://irep.iium.edu.my/79233/
http://irep.iium.edu.my/79233/
http://irep.iium.edu.my/79233/
http://irep.iium.edu.my/79233/19/79233_Compressed%20channel%20estimation%20for%20massive_article_new.pdf
http://irep.iium.edu.my/79233/2/79233_Compressed%20channel%20estimation%20_scopus.pdf
http://irep.iium.edu.my/79233/3/79233_Compressed%20channel%20estimation%20_wos.pdf